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All Journal Emerging Science Journal
Kalaiarasi Sonai Muthu Anbananthen
Centre for Advanced Analytics, CoE for Artificial Intelligence & Faculty of Information Science and Technology, Multimedia University, Melaka 75450
Environmental Science

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IoT-Driven Emotional Data Analytics for Medical Applications: Insights and Innovations D. Akila; Souvik Pal; M. Vijayarani; Bikramjit Sarkar; Kalaiarasi Sonai Muthu Anbananthen; Saravanan Muthaiyah
Emerging Science Journal Vol. 10 No. 1 (2026): February
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2026-010-01-05

Abstract

This study introduces the Internet of Things-based Emotional State Detection Model (IoT-ESDM), a comprehensive and intelligent emotional computing framework aimed at detecting and managing anxiety-related behavior in healthcare environments. The model leverages a multi-modal approach that combines facial expression analysis, physiological signal monitoring, and AI-driven classification to accurately identify emotional states in real time. Core components of the system include fuzzy color filtering, histogram analysis, and virtual face modeling, which work together to extract relevant emotional features from input data. These features are then analyzed to provide adaptive, personalized feedback to patients or caregivers, enhancing emotional well-being support. Experimental results demonstrate the superior performance of IoT-ESDM over existing emotion detection systems. The model achieved a feedback ratio of 97.54%, accessibility ratio of 95.3%, detection accuracy of 92.7%, and a classification accuracy of 98.13%. Additionally, it showed a quality assurance rate of 94.13%, contributed to a 29.1% reduction in anxiety levels, and yielded a health outcome ratio of 94.5%. These metrics validate the system's effectiveness in clinical and real-world applications. The success of IoT-ESDM highlights its potential as a powerful tool for emotion-aware AI interventions, paving the way for future advancements in mental health monitoring and personalized healthcare solutions.
Gold Price Forecasting Using Machine Learning Models with Hyperparameter Optimization for Inflation Hedging Joan Sim Pei Suan; Kalaiarasi Sonai Muthu Anbananthen; Raj Kumar Kanan
Emerging Science Journal Vol. 10 No. 3 (2026): June
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2026-010-03-016

Abstract

Gold serves as a hedge against inflation, particularly in emerging markets such as Malaysia, where macroeconomic volatility is pronounced. This study evaluates the predictive performance of six machine learning models; comprising ensemble models (Random Forest, XGBoost, Gradient Boosting Machine, and LightGBM) and deep learning models (Long Short-Term Memory and Gated Recurrent Units) in forecasting Malaysia's gold prices using monthly macroeconomic data from 2009 to 2024. Key indicators include inflation rates, interest rates, exchange rates, oil prices, and stock indices. Hyperparameter tuning is performed using the Optuna framework by comparing three optimization strategies: Tree-structured Parzen Estimator (TPE), Grid Search, and Covariance Matrix Adaptation Evolution Strategy (CMA-ES). Experimental results show that Gradient Boosting, optimized via CMA-ES, achieves the best performance (RMSE = 101.26, R² = 0.9972) using the complete feature set. While deep learning models demonstrate improvements following optimization, ensemble models consistently outperform them due to better alignment with the static, cross-sectional nature of the dataset. Feature importance analysis identifies GP_Low, GP_High, and both domestic and international inflation and interest rates as the most significant predictors. This study contributes by benchmarking ensemble and deep learning models, evaluating multiple hyperparameter optimization strategies, and identifying key macroeconomic indicators relevant to gold price forecasting. The findings provide valuable insights for investors, financial analysts, and policymakers in economies sensitive to inflation.
Co-Authors Bikramjit Sarkar D. Akila Joan Sim Pei Suan M. Vijayarani Raj Kumar Kanan Saravanan Muthaiyah Souvik Pal